Low-complexity multi-head detection for multi-track partial response and two-dimensional recording channels

Author(s):  
E.K.S. Au ◽  
Wai Ho Mow
2018 ◽  
Vol 41 (3) ◽  
pp. 875-882 ◽  
Author(s):  
Jian-Feng Wu ◽  
Shang-Shang He ◽  
Feng Wang ◽  
Yu Wang ◽  
Xin-Gang Zhao ◽  
...  

In the readout circuits of the two-dimensional (2-D) resistive sensor arrays, various auxiliary electrical components were used to reduce their crosstalk errors but resulted in increased circuit complexity. Readout circuits with low-complexity structures were necessary for wearable electronic applications. With only several resistors and a microcontroller, readout circuit based on resistance matrix approach (RMA) achieved low complexity but suffered from small resistance range and large measurement error caused by the output ports’ internal resistances of the microcontroller. For suppressing those negative effects, we firstly proposed an improved resistance matrix approach (IRMA) by additionally sampling the voltages on all driving row electrodes in the RMA. Then the effects of the output ports’ internal resistances and the analog-to-digital converter’s accuracy for the RMA and the IRMA were simulated respectively with NI Multisim 12. Moreover, a prototype readout circuit based on the IRMA was designed and tested in actual experiments. The experimental results demonstrated that the IRMA, though it required more sampling channels and more computations, could be used in those applications needing low complexity, small measurement error and wide resistance range.


1971 ◽  
Vol 1 (2) ◽  
pp. 99-112 ◽  
Author(s):  
J. K. Jeglum ◽  
C. F. Wehrhahn ◽  
J. M. A. Swan

Data from a survey of lowland, mainly peatland, vegetation were subjected to environmental ordination based on measurements of water level and water conductivity, and to vegetational ordination derived from principal component analysis (P.C.A.). Analyzed were the total set of the data ("all types"), half sets ("nonwoody" and "woody types") and quarter sets (stands of "marshes", "meadows", "shrub fens", and "other woody types"); the number of distinct physiognomic groups in a set of data, and presumably the amount of contained heterogeneity, decreased at each segmentation.The effectiveness of the ordination models was tested by correlating measured distances in two-dimensional ordination models with 2W/(A + B) indices of vegetational similarity for randomly selected pairs of types or stands. As the physiognomic complexity decreased, the effectiveness of the P.C.A. vegetational ordination increased whereas that of the environmental ordination decreased. The environmental ordination seemed most appropriate to the data encompassing high complexity (total data set), while the P.C.A. vegetational ordination seemed most appropriate to data with low complexity (quarter sets of the data).


2002 ◽  
Vol 38 (16) ◽  
pp. 928 ◽  
Author(s):  
J. Liu ◽  
H. Song ◽  
B.V.K. Vijaya Kumar

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